---
title: "Data Visualization Project - Shreyas Meher"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
theme: lumen
---
```{r setup, include=FALSE}
library(flexdashboard)
footprint <- read.csv("countries.csv")
library(rgdal) # plot map
library(leaflet) # plot map
library(dplyr) # data wrangling
library(tidyr) # data wrangling
library(ggplot2) # plot
library(plotly) # interactive plot
library(shiny) # shiny
library(shinydashboard) # shiny dashboard
library(ggthemes) # themes for ggplot2
library(extrafont) # fonts for ggplot2
```
Top 10 countries {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Countries with high Ecological Footprints
- From the resultant plot, we can see that most of the countries in the top 10 of Ecological Footprint data are developed nations - with only Qatar, Trinidad and Tobago and Oman being the lesser developed of the 10.
- This is corroborated by research by Niccolucci, V., et al. (2012) and other researchers who find out that the developed world has a few factors which lead to overuse of natural resources due to industrialization and a better ability at extracting resources more efficiently.
- Ecological Footprint - A measure of how much area of biologically productive land and water an individual, population, or activity requires to produce all the resources it consumes and to absorb the waste it generates, using prevailing technology and resource management practices. The Ecological Footprint is usually measured in global hectares.
- Biocapacity - The capacity of ecosystems to regenerate what people demand from those surfaces. Life, including human life, competes for space. The biocapacity of a surface represents its ability to renew what people demand. Biocapacity is therefore the ecosystems' capacity to produce biological materials used by people and to absorb waste material generated by humans, under current management schemes and extraction technologies.
Column {data-width=400}
------------------------------------------------------------------------------
### Top 10 Ecological Footprint countries
```{r}
# Selecting top 10 countries and arranging by ecological footprint
top_10 <- top_n(footprint, n=10, Total.Ecological.Footprint) %>%
arrange(desc(Total.Ecological.Footprint))
p3<-ggplot(top_10,aes(x= reorder(Country, -Total.Ecological.Footprint), y=Total.Ecological.Footprint, fill = Total.Ecological.Footprint))+
scale_fill_gradient(
low = "#BE6741",
high = "#FB0421",
space = "Lab",
na.value = "grey50",
guide = "colourbar",
aesthetics = "fill"
)+
geom_col()+scale_x_discrete(guide = guide_axis(n.dodge=3))+
theme(axis.text.x = element_text(angle = 40, vjust = 1, hjust =1),
legend.position = "none")+
# Changing labels
labs(y = "Total Ecological Footprint in global hectares",
x = '',
title = "Highest Ecological Footprint Countries",
caption = 'Source: Global Ecoogical Footprint, Global Footprint Network')
p3 + theme_minimal()
```
Column {data-width=400}
------------------------------------------------------------------------------
### Top 10 Biocapacity countries
```{r}
# Selecting top 10 countries and arranging by Biocapacity
top2_10 <- top_n(footprint, n=10, Total.Biocapacity) %>%
arrange(desc(Total.Biocapacity))
# Plotting
p2<-ggplot(top2_10,aes(x= reorder(Country, -Total.Biocapacity), y=Total.Biocapacity, fill = Total.Biocapacity))+
scale_fill_gradient(
low = "#EDBF2B",
high = "#067736",
space = "Lab",
na.value = "grey50",
guide = "colourbar",
aesthetics = "fill"
)+
geom_col()+scale_x_discrete(guide = guide_axis(n.dodge=3))+
theme(axis.text.x = element_text(angle = 40, vjust = 1, hjust =1),
legend.position = "none")+
# Changing labels
labs(y = "Total Biocapacity in global hectares",
x = '',
title = "Highest Biocapacity Countries",
caption = 'Source: Global Ecoogical Footprint, Global Footprint Network')
p2 + theme_minimal()
```
Region-wise visualization {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Ecological Footprint & Biocapacity by Region
- Continuing on, we can clearly see the difference in Ecological Footprint versus the Biocapacity of the various regions in the world.
- The dataset was mutated so as to help with the visualization of this variable, and the data was made interactive so as to give the exact values if you hover your mouse over the columns.
- Here, we can tell that the European Union, on account of the number of developed nations it consists of along with Central Asia have the largest Ecological Footprint.
- Conversely, Latin America and Africa - which are not on the higher Ecological Footprint ranks, have a greater Biocapacity.
Column {data-width=400}
------------------------------------------------------------------------------
### Ecological Footprint by Region
```{r}
footprint <- footprint %>%
mutate(Country = as.character(Country), GDP.per.Capita = as.numeric(gsub("[$,]",
"", footprint$GDP.per.Capita)), HDI = round(HDI,
2), Countries.Required = round(Countries.Required,
2), Biocapacity.Deficit = as.factor(ifelse(Biocapacity.Deficit >
0, "Reserve", "Deficit"))) %>%
rename(Status = Biocapacity.Deficit) %>%
select(-c(Data.Quality)) %>%
drop_na()
# Ecological Footprint and Biocapacity plots
ef_region <- footprint %>%
group_by(Region) %>%
summarize(Ecological.Footprint = sum(Total.Ecological.Footprint)) %>%
arrange(desc(Ecological.Footprint)) %>%
mutate(text = paste0("Ecological Footprint: ",
Ecological.Footprint, " gha"))
ef_reg_plot <- ggplot(ef_region, aes(x = reorder(Region,
Ecological.Footprint), y = Ecological.Footprint,
text = text)) + geom_col(aes(fill = Ecological.Footprint),
show.legend = F) + coord_flip() + labs(title = "Ecological Footprint by Region",
y = "global hectares (gha)", x = NULL) + scale_y_continuous(limits = c(0,
150), breaks = seq(0, 150, 25)) + scale_fill_gradient(low = "#F78181",
high = "#3B0B0B") + theme(plot.title = element_text(face = "bold",
size = 14, hjust = 0.04), axis.ticks.y = element_blank(),
panel.background = element_rect(fill = "#ffffff"),
panel.grid.major.x = element_line(colour = "grey"),
axis.line.x = element_line(color = "grey"), axis.text = element_text(size = 10,
colour = "black"))
ggplotly(ef_reg_plot, tooltip = "text")
```
Column {data-width=400}
------------------------------------------------------------------------------
### Biocapacity by Region
```{r}
b_region <- footprint %>%
group_by(Region) %>%
summarize(Biocapacity = sum(Total.Biocapacity)) %>%
arrange(desc(Biocapacity)) %>%
mutate(text = paste0("Biocapacity: ", Biocapacity,
" gha"))
b_reg_plot <- ggplot(b_region, aes(x = reorder(Region,
Biocapacity), y = Biocapacity, text = text)) +
geom_col(aes(fill = Biocapacity), show.legend = F) +
coord_flip() + labs(title = "Biocapacity by Region",
y = "global hectares (gha)", x = NULL) + scale_y_continuous(limits = c(0,
275), breaks = seq(0, 250, 50)) + scale_fill_gradient(low = "#9AFE2E",
high = "#0B6121") + theme(plot.title = element_text(face = "bold",
size = 14, hjust = 0.04), axis.ticks.y = element_blank(),
panel.background = element_rect(fill = "#ffffff"),
panel.grid.major.x = element_line(colour = "grey"),
axis.line.x = element_line(color = "grey"), axis.text = element_text(size = 10,
colour = "black"))
ggplotly(b_reg_plot, tooltip = "text")
```
Footprint & GDP {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Ecological Footprint & GDP
- To find out the relationship between the GDP of the country and the Ecological footprint, I am using a simple linear plot to estimate the relationship.
- With the output, we can see that as GDP increases, the Ecological footprint increases correspondingly.
- The data used is from the Global Footprint Network website and the data set is freely accessible to all.
- I created a unique data set gleaning from a few variables from the GFN along with World Bank data.
- The result has considerable implications, with research done by Mattila (2012) & Toth & Szigeti (2016) echoing similar concerns regarding the over-exploitation of natural resources in the race to obtain higher economic growth.
Column {data-width=400}
-----------------------------------------------------------------------
### Ecological Footprint ~ GDP
```{r}
# data loading
# GDP and Ecological footprint
p1<-ggplot(footprint, aes(x=HDI, y=Total.Ecological.Footprint))+
geom_point()+
geom_smooth(method = "lm", se = FALSE)+
labs(y = "Total Ecological Footprint in global hectares",
x = 'GDP',
title = "Ecological Footprint according to GDP",
caption = 'Source: Global Ecoogical Footprint, Global Footprint Network')
p1 + theme_minimal()
```
Investigating the relationship {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Correlation between GDP and Ecological Footprint
- This is a much nicer plot with interactive elements within it.
- The progression with this graph is the added interactivity and the data that is included when you hover your mouse over the points.
- You can zoom into a section by selecting a part of the plot with a mouse click, and then focus on certain data points.
- The deficit/reserve is indicated by a green or purple circle, which gives us an idea of the overall trend while keeping into account the GDP on the X-axis at the same time.
- While in the previous plot, we saw a linear relationship, here we can focus on other variables along with the Ecological Footprint.
- Sweden and Denmark act as a interesting contrast, being neighboring nations with similar GDP but Denmark on account of its' small size is at a deficit while Sweden has a Ecological Reserve.
Column {data-width=400}
-----------------------------------------------------------------------
### GDP and Ecological Footprint
```{r}
# Scatterplot between GDP and Ecological Footprint
scat_plot_data <- footprint %>%
select(Country, Population.millions, GDP.per.Capita,
HDI, Total.Ecological.Footprint, Status) %>%
rename(Population.in.millions = Population.millions,
Human.Development.Index = HDI, Ecological.Footprint = Total.Ecological.Footprint) %>%
mutate(text = paste0("Country: ", Country, "
",
"HDI: ", Human.Development.Index, "
", "Ecological Footprint: ",
Ecological.Footprint, "
", "GDP per Capita: ",
"$", GDP.per.Capita))
scat_plot <- ggplot(scat_plot_data, aes(x = GDP.per.Capita,
y = Ecological.Footprint, text = text)) + geom_smooth(col = "#61380B",
size = 0.7) + geom_point(aes(color = Status, size = GDP.per.Capita)) +
scale_y_continuous(limits = c(0, 18)) + scale_color_manual(values = c("#FF2200",
"#32A852")) + labs(title = "GDP on Ecological Footprint",
y = "Ecological Footprint", x = "GDP Per Capita") +
theme(plot.title = element_text(face = "bold",
size = 14, hjust = 0), panel.background = element_rect(fill = "#ffffff"),
panel.grid.major.x = element_line(colour = "grey"),
panel.grid.major.y = element_line(colour = "grey"),
axis.line.x = element_line(color = "grey"),
axis.line.y = element_line(color = "grey"),
axis.text = element_text(size = 10, colour = "black"),
legend.title = element_blank())
ggplotly(scat_plot, tooltip = "text") %>%
layout(legend = list(orientation = "v", y = 1,
x = 0))
```
